Department of Computer Science and Technology, Harbin Institute of Technology, China.
Department of Computer Science and Technology, Harbin Institute of Technology, Shenzhen, China.
Artif Intell Med. 2022 Aug;130:102329. doi: 10.1016/j.artmed.2022.102329. Epub 2022 Jun 10.
Knowledge graph (KG) is a multi-relational data that has proven valuable for many tasks including decision making and semantic search. In this paper, we present GTGAT (Gated Tree-based Graph Attention), a method for tackling the problems of transductive and inductive reasoning in generalized KGs. Based on recent advancement of graph attention network (GAT), we develop a gated tree-based method to distill valuable information in neighborhood via hierarchical-aware and semantic-aware attention mechanism. Our approach not only addresses several key challenges of GAT but is also capable of undertaking multiple downstream tasks. Experimental results have revealed that our proposed GTGAT has matched state-of-the-art approaches across transductive benchmarks on the Cora, Citeseer, and electronic medical record networks (EMRNet). Meanwhile, the inductive experiments on medical knowledge graphs show that GTGAT surpasses the best competing methods for personalized disease diagnosis.
知识图谱 (KG) 是一种多关系数据，已被证明对许多任务（包括决策和语义搜索）非常有价值。在本文中，我们提出了 GTGAT（基于门控树的图注意力），这是一种解决广义 KG 中转导推理和归纳推理问题的方法。基于图注意力网络 (GAT) 的最新进展，我们开发了一种基于门控树的方法，通过分层感知和语义感知注意力机制从邻域中提取有价值的信息。我们的方法不仅解决了 GAT 的几个关键挑战，而且还能够承担多个下游任务。实验结果表明，我们提出的 GTGAT 在 Cora、Citeseer 和电子病历网络 (EMRNet) 等转导基准上与最先进的方法相匹配。同时，在医学知识图谱上的归纳实验表明，GTGAT 在个性化疾病诊断方面优于最佳竞争方法。
